A Python package for causal inference using Synthetic Controls

Statistical inference and graphical procedures for RD designs using local polynomial and partitioning regression methods.

Synthetic difference in differences for Python

📊 🌐 🧑‍🏫 Website for graduate-level course on program evaluation and causal inference using R, built with Quarto

📊 🌐 🧑‍🏫 Website for graduate-level course on program evaluation and causal inference using R, built with Quarto

Regression Discontinuity Design Software Packages

Manipulation testing using local polynomial density methods.

📊 🌐 🧑‍🏫 Website for graduate-level course on program evaluation and causal inference using R, built with Quarto

Finite-sample inference for RD designs using local randomization and related methods.

Power and sample size calculations for RD designs using robust bias-corrected local polynomial inference.

Estimation, inference, RD Plots, and extrapolation with multiple cutoffs and multiple scores RD designs.

Curso de Econometría con un aplicaciones en Python

Implementation of Van Dijcke, David. "Regression Discontinuity Design with Distribution-Valued Outcomes." Manuscript.

Open Course on Program Evaluation

syntCF is an R package that provides a set of tools to estimate the effect of a program or a policy using a robust time series synthetic counterfactual approach coupled with the double difference estimator within a Machine Learning framework.

A PyTorch implementation of the "robust" synthetic control model

Bluecoats is a closed-loop, human-centric measurement and response program coordinating training, resources, and operational mechanisms to empower health system staff and management to systematically diagnose issues and develop custom solutions that improve wellbeing, streamline operations, and bolster the financial health of the organization.

Program Evaluation with Non-Parametric Statistics and Hypothesis Testing

Home for Anthony D'Agostino's professional webpage